Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis

Abstract

We present a gene-level regulatory model, single-cell ATAC + RNA linking (SCARlink), which predicts single-cell gene expression and links enhancers to target genes using multi-ome (scRNA-seq and scATAC–seq co-assay) sequencing data. The approach uses regularized Poisson regression on tile-level accessibility data to jointly model all regulatory effects at a gene locus, avoiding the limitations of pairwise gene–peak correlations and dependence on peak calling. SCARlink outperformed existing gene scoring methods for imputing gene expression from chromatin accessibility across high-coverage multi-ome datasets while giving comparable to improved performance on low-coverage datasets. Shapley value analysis on trained models identified cell-type-specific gene enhancers that are validated by promoter capture Hi-C and are 11× to 15× and 5× to 12× enriched in fine-mapped eQTLs and fine-mapped genome-wide association study (GWAS) variants, respectively. We further show that SCARlink-predicted and observed gene expression vectors provide a robust way to compute a chromatin potential vector field to enable developmental trajectory analysis.

A nuclear RNA degradation code for eukaryotic transcriptome surveillance

“…49 We selected those SNPs whose PIP score was at least 0.2, which has been used in previous studies to select causal variants. 50 Using these parameters, we identified 21 SNPs that created a novel 5′ ss (Fig. 5E) and were associated with a wide range of diseases/disorders, including ankylosing spondylitis and depression (Fig.…”